Coming from Peru to Scotland was not only a cultural shock but an
environmental one, if you will. I had never experienced such strikingly
beautiful but equally emotionally debilitating winters. Here is when I
first heard the term ‘Seasonal Depression’ being thrown around at this
time of the year, between the end of Autumn and the cusp of Winter.
Whereas it’s a passing comment from a friend, a sitcom joke, or a
genuine Google search during those short, dark, and cold winter days,
‘Seasonal Depression’, or medically classified as Seasonal Affective
Disorder (SAD), has affected almost everyone I know here.
Therefore, this investigation seeks to quantify the verbal leads I have
stumbled upon for years during conversations with friends and
acquaintances.
SAD is widely discussed in literature, with a focus in populations living at high latitudes such as Denmark, Sweden and Iceland (REFERENCE et al., DATE). Mainland Scotland regionally situates itself on par with the southernmost areas of Scandinavian countries, with Edinburgh and Copenhagen being latitudinal buddies. However, the Scottish Isles (i.e, Orkney, Shetland, etc) are further north and are roughly comparable in latitude to Norwegian cities such as Bergen and Oslo. Hence, making the nature of this investigation as interesting as it close to my personal interests.
This report focuses on investigating whether antidepressant
medication prescribing in Scotland shows seasonal patterns and whether
those patterns are related to regional daylight exposure and deprivation
levels.
The research question to be explored asks:
To what extent do seasonal variations in daylight hours affect antidepressant medication prescribing across Scottish NHS Health Boards?
For this investigation, prescription data is taken as a direct inferential measure of the population-level mental health burden.
This report puts forward an overarching hypothesis:
Null (H0): Antidepressant prescription rates do not have seasonal patterns.
Alternative (Ha): Antidepressant prescription rates have seasonal patterns.
Where within Ha, the following is proposed:
1a. Prescription rates increase during shorter months with shorter daylight hours and colder temperatures (autumn and winter) and decrease during those with longer daylight hours and warmer temperature (spring and summer).
In the assumption that Ha is true, this report proposed two further sub-hypotheses that further enrich the multi-factorial lens through which seasonal effects inlfuence prescription rates:
I. The latitude of prescribing NHS Health Boards further influences the seasonality of antidepressant prescription rates, the more northern a health board is, the heavier the impact of these seasonal patterns.
II. Areas with a higher socioeconomic deprivation index (SMID) will have a consistently higher benchmark antidepressant use regardless of season, making the impact of varying daylight hours and temperature be even greater.
Thus, the report tests whether: a) antidepressant prescriptions are higher in autumn/winter than in spring/summer, b) geographic latitude/region affects seasonal patterns, and c) more deprived areas show higher baseline antidepressant prescribing.
Variables were regionally and seasonally categorised according to Met Office UK guidelines. For information on specific categorisation see Appendix 1.1 and 1.2.
Raw data from multiple institutional websites include all medications prescribed by NHS Health Boards. To differentiate and include only Antidepressant prescriptions, only prescriptions with a BNF item code starting with ‘0403’ were used.
This report is a multi-factorial exploration of the environmental and social dimensions of antidepressant prescription rates in Scotland given that it’s analysis focuses on three distinct aspects as shown below.
The investigation window used is from March 2024 to February 2025
(inclusive of March 1st, 2024 - February 28th, 2025) to assess the
potential cyclical nature of results and make adequate comparisons
between seasons.
All links mentioned below are included in the code within
Methodology.
docs/data. See original
TXT files in Appendix 1.3.library(tidyverse)
library(here) # for the upkeep of the directory structure
library(janitor) # for data cleaning
library(lubridate)
library(gt) # for table building
library(sf) # for geospatial visualisation
library(ggplot2)
library(ggtext)
library(patchwork)
library(plotly)
library(cowplot)
library(stringr)
Utilising janitor package by using the
clean_names() function to have uniform names throughout
datasets once the all prescription datasets are loaded.
urls_prescr <- list(
prescr_jan_june_2024 <- "https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/f0df380b-3f9b-4536-bb87-569e189b727a/download/hb_pitc2024_01_06-1.csv",
prescr_july_dec_2024 <- "https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/f3b9f2e2-66c0-4310-9b8e-734781d2ed0a/download/hb_pitc2024_07_12-1.csv",
prescr_jan_june_2025 <- "https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/9de908b3-9c28-4cc3-aa32-72350a0579d1/download/hb_pitc2025_01_06.csv")
# Reads all Health Board prescription data in a loop to avoid repetition
prescr_list <- map(urls_prescr,
~read_csv(.x) %>%
clean_names())
# Ainds together everything in a single tibble
prescr_raw <- bind_rows(prescr_list, .id = "source_file") %>%
mutate(paid_date_month = str_trim(as.character(paid_date_month))) %>%
select(-source_file)
glimpse(prescr_raw)
## Rows: 2,249,380
## Columns: 9
## $ hbt <chr> "S08000015", "S08000015", "S08000015", "S0800001…
## $ dmd_code <dbl> 1.001011e+15, 1.001411e+15, 1.001811e+15, 1.0018…
## $ bnf_item_code <chr> "0603020J0AAAEAE", "1001010P0AAAHAH", "1310012F0…
## $ bnf_item_description <chr> "HYDROCORTISONE 20MG TABLETS", "NAPROXEN 250MG G…
## $ prescribed_type <chr> "VMP", "VMP", "VMP", "VMPP", "VMP", "VMPP", "VMP…
## $ number_of_paid_items <dbl> 25, 53, 275, 1, 181, 2, 487, 1432, 66, 1, 1, 283…
## $ paid_quantity <dbl> 1244, 4046, 4695, 15, 25320, 240, 24924, 65820, …
## $ gross_ingredient_cost <dbl> 145.58, 187.17, 1111.15, 3.55, 4093.40, 38.80, 5…
## $ paid_date_month <chr> "202401", "202401", "202401", "202401", "202401"…
Filtering out by “bnf_item_code” to keep only those prescriptions that are antidepressants (codes starting with ‘0403’) and that were prescribed within investigation window.
# Only keeping antidepressant codes and aggregating prescriptions per HB per month
prescr_monthly <- prescr_raw %>%
filter(!is.na(bnf_item_code)) %>%
filter(str_detect(bnf_item_code, "^0403")) %>%
mutate(paid_date_month = as.integer(paid_date_month)) %>%
group_by(hbt, paid_date_month) %>%
summarise(number_of_items = sum(number_of_paid_items, na.rm = TRUE)) %>%
arrange(paid_date_month)
# Subsetting to fit our investigation window
prescr_monthly <- prescr_monthly %>%
filter(paid_date_month >= 202403, paid_date_month <= 202502)
prescr_monthly %>%
summarise(rows = n(), min_month = min(paid_date_month), max_month = max(paid_date_month))
## # A tibble: 15 × 4
## hbt rows min_month max_month
## <chr> <int> <int> <int>
## 1 S08000015 12 202403 202502
## 2 S08000016 12 202403 202502
## 3 S08000017 12 202403 202502
## 4 S08000019 12 202403 202502
## 5 S08000020 12 202403 202502
## 6 S08000022 12 202403 202502
## 7 S08000024 12 202403 202502
## 8 S08000025 12 202403 202502
## 9 S08000026 12 202403 202502
## 10 S08000028 12 202403 202502
## 11 S08000029 12 202403 202502
## 12 S08000030 12 202403 202502
## 13 S08000031 12 202403 202502
## 14 S08000032 12 202403 202502
## 15 SB0806 1 202412 202412
spr_months_202425 <- c(202403, 202404, 202405)
sum_months_202425 <- c(202406, 202407, 202408)
aut_months_202425 <- c(202409, 202410, 202411)
win_months_202425 <- c(202412, 202501, 202502)
seasons_202425 <- tibble(
paid_date_month = c(spr_months_202425, sum_months_202425, aut_months_202425, win_months_202425),
season = c(rep("Spring", length(spr_months_202425)),
rep("Summer", length(sum_months_202425)),
rep("Autumn", length(aut_months_202425)),
rep("Winter", length(win_months_202425))))
# Health Board official NHS names list
hb_names <- read_csv("https://www.opendata.nhs.scot/dataset/9f942fdb-e59e-44f5-b534-d6e17229cc7b/resource/652ff726-e676-4a20-abda-435b98dd7bdc/download/hb14_hb19.csv") %>%
clean_names()
# Met Office regional mapping
north_hb <- c("NHS Highland", "NHS Western Isles", "NHS Orkney", "NHS Shetland")
east_hb <- c("NHS Borders", "NHS Lothian", "NHS Fife", "NHS Tayside", "NHS Grampian", "NHS Forth Valley")
west_hb <- c("NHS Ayrshire and Arran", "NHS Dumfries and Galloway", "NHS Greater Glasgow and Clyde", "NHS Lanarkshire")
metoffice_hb_region <- tibble(
hb_name = c(north_hb, east_hb, west_hb),
region = c(rep("North", length(north_hb)),
rep("East", length(east_hb)),
rep("West", length(west_hb))))
# Health Boards per region according to Met Office scottish territorial classifications
hb_regional <- hb_names %>%
full_join(metoffice_hb_region, by = "hb_name") %>%
select(-c(hb_date_archived, hb_date_archived, hb_date_enacted, country))
From October 2024 data file. The data file chosen is deliberate as October 2024 marks approximately half-way of the investigation window.
hb_pop <- read_csv("https://www.opendata.nhs.scot/dataset/e3300e98-cdd2-4f4e-a24e-06ee14fcc66c/resource/cec9341e-ccba-4c71-afe4-a614f5e97b9f/download/practice_listsizes_oct2024-open-data.csv") %>%
clean_names() %>%
select(hb, sex, all_ages) %>%
filter(!sex %in% c("Male", "Female")) %>%
group_by(hb) %>%
summarise(hb_population = sum(all_ages, na.rm = TRUE)) %>%
ungroup()
prescr_seasonal <- prescr_monthly %>%
full_join(hb_regional %>%
select(hb, hb_name, region), by = join_by(hbt == hb)) %>%
full_join(hb_pop, by = join_by(hbt == hb)) %>%
full_join(seasons_202425, by = join_by(paid_date_month))
full_join() there are 4 rows with NA in
prescription data and population (paid_date_month, number_or_items, and
hb_population). Upon further investigation and a look at the object
hb_names, these were shown to have had their hbt numbers archived in
2018 and 2019, making them easily removable from our data.prescr_seasonal <- prescr_seasonal %>%
filter(!is.na(hb_name)) %>%
filter(!is.na(paid_date_month))
# checking if there is a missing region or population
prescr_seasonal %>%
filter(is.na(region) | is.na(hb_population)) # tibble of 0 x 7 shows we have eliminated all NAs
## # A tibble: 0 × 7
## # Groups: hbt [0]
## # ℹ 7 variables: hbt <chr>, paid_date_month <dbl>, number_of_items <dbl>,
## # hb_name <chr>, region <chr>, hb_population <dbl>, season <chr>
A calculation of items_per_1000_people was be made to allow for population weighting of prescription items. This is because all NHS Health Boards have different populations, thus, comparing their “number_of_items” solely would be affected by population sizes.
prescr_seasonal_standard <- prescr_seasonal %>%
mutate(items_per_1000 = (number_of_items/hb_population)*1000)
This is somewhat challenging. Given the nature of the files the Met
Office has available (.txt), I converted them into .csv files using
Excel. These files can be found in the docs\data folder
attached.. Upon inspection of the data, one can notice that there are
specific columns for each season apart from one for each month. This
report uses seasonal data to make the merging processes easier. Building
a function had to be done to avoid repeting the same wrangling for each
region.
Note: these .csv files contain columns named
spr,sum,aut,winand a year column for each year and season
# Reading all CSV files first
daylight_north <- read_table(here("docs", "data", "R_north_scotland_sunshine.csv")) %>%
clean_names()
daylight_east <- read_table(here("docs","data", "R_east_scotland_sunshine.csv")) %>%
clean_names()
daylight_west <- read_table(here("docs","data","R_west_scotland_sunshine.csv")) %>%
clean_names()
# Making a function to avoid code repetition for each .csv file
daylight_season_function <- function(data, region_name, year_filter, season_cols = c("win", "spr", "sum", "aut"), year_cols = c("year_12", "year_13", "year_14", "year_15"), full_season_names = c("Winter", "Spring", "Summer", "Autumn")) {
# built-in checker
if(length(season_cols)!= length(year_cols)) stop("season_cols and year_cols need to have the same length")
if(length(season_cols)!= length(full_season_names)) stop("full_season_names and season_cols need to have the same length")
# processing each individual season
season_list <- map2(season_cols, year_cols, ~ {
data %>%
select(all_of(.x), all_of(.y)) %>%
filter(.data[[.y]] == year_filter) %>%
rename(year = all_of(.y))
})
# joining all four seasons together
season_complete <- reduce(season_list, full_join, by = "year") %>%
relocate(all_of(season_cols), .after = last_col()) %>%
mutate(across(all_of(season_cols), as.numeric)) %>%
pivot_longer(cols = all_of(season_cols), names_to = "season", values_to = "daylight_hrs") %>%
mutate(season = recode(season, !!!set_names(full_season_names, season_cols)),
region = region_name) %>%
arrange(year, factor(season, levels = full_season_names)) %>%
filter(!is.na(daylight_hrs))
return(season_complete)
}
daylight_season_functiondaylight_season_function() was used to wrangle and
select specific data from each Met Office UK regional daylight dataset.
all_seasons_daylight contains all daylight hours per season
per region for the duration of the investigation window.
# Using the function for each region and making sure the "year" category is a character
season_daylight_north <- daylight_season_function(daylight_north, "North", "2024")
season_daylight_north <- season_daylight_north %>%
mutate(year = as.character(year))
season_daylight_east <- daylight_season_function(daylight_east, "East", "2024")
season_daylight_east <- season_daylight_east %>%
mutate(year = as.character(year))
season_daylight_west <- daylight_season_function(daylight_west, "West", "2024")
season_daylight_west <- season_daylight_west %>%
mutate(year = as.character(year))
# Merging all regional daylight data for each season
all_seasons_daylight <- bind_rows(season_daylight_north, season_daylight_east, season_daylight_west)
all_seasons_daylight
## # A tibble: 12 × 4
## year season daylight_hrs region
## <chr> <chr> <dbl> <chr>
## 1 2024 Winter 122. North
## 2 2024 Spring 375. North
## 3 2024 Summer 330. North
## 4 2024 Autumn 230. North
## 5 2024 Winter 164. East
## 6 2024 Spring 362. East
## 7 2024 Summer 453. East
## 8 2024 Autumn 269. East
## 9 2024 Winter 137. West
## 10 2024 Spring 367. West
## 11 2024 Summer 403. West
## 12 2024 Autumn 230. West
The Scottish Index of Multiple Deprivation (SIMD) data was merged, wrangled and saved for later analysis.
# Median rank per Health Board was used to summarise the distribution of deprivation
smid_raw <- read_csv("https://www.opendata.nhs.scot/dataset/78d41fa9-1a62-4f7b-9edb-3e8522a93378/resource/acade396-8430-4b34-895a-b3e757fa346e/download/simd2020v2_22062020.csv") %>%
clean_names()
smid_hb <- smid_raw %>%
select(hb, simd2020v2rank) %>%
group_by(hb) %>%
summarise(SMID_median_rank = median(simd2020v2rank, na.rm = TRUE)) %>%
ungroup()
# Joining daylight, SMID and seasonal prescription data
analysis_prescr_smid <- prescr_seasonal_standard %>%
full_join(all_seasons_daylight, by = join_by(season, region)) %>%
full_join(smid_hb, by = join_by(hbt == hb))
# Checking if any NAs are present
analysis_prescr_smid %>%
summarise(missing_daylight = sum(is.na(daylight_hrs)), missing_smid = sum(is.na(SMID_median_rank))) # result should show a 14 x 3 tibble with value zero (0) for each
## # A tibble: 14 × 3
## hbt missing_daylight missing_smid
## <chr> <int> <int>
## 1 S08000015 0 0
## 2 S08000016 0 0
## 3 S08000017 0 0
## 4 S08000019 0 0
## 5 S08000020 0 0
## 6 S08000022 0 0
## 7 S08000024 0 0
## 8 S08000025 0 0
## 9 S08000026 0 0
## 10 S08000028 0 0
## 11 S08000029 0 0
## 12 S08000030 0 0
## 13 S08000031 0 0
## 14 S08000032 0 0
The NHS Health Boards shapefile was merged, wrangled and saved for geospatial analysis.
hb_shp_geo <- st_read(here("docs","data", "Week6_NHS_healthboards_2019.shp")) %>%
clean_names()
## Reading layer `Week6_NHS_healthboards_2019' from data source
## `/Users/florenciasolorzano/Documents/data_science/B218332/docs/data/Week6_NHS_healthboards_2019.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 14 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 7564.996 ymin: 530635.8 xmax: 468754.8 ymax: 1218625
## Projected CRS: OSGB36 / British National Grid
analysis_geo_prescr <- analysis_prescr_smid %>%
group_by(hbt, season, SMID_median_rank) %>%
summarise(av_items_per_1000 = mean(items_per_1000, na.rm = TRUE), av_daylight = mean(daylight_hrs)) %>%
ungroup() %>%
full_join(hb_shp_geo, by = join_by(hbt == hb_code)) %>%
st_as_sf()
A seasonal summary table by region using gt():
full_seasonal_table <- analysis_prescr_smid %>%
group_by(region, season) %>%
summarise(av_daylight = mean(daylight_hrs, na.rm = TRUE), av_items_per_1000 = mean(items_per_1000, na.rm = TRUE)) %>%
ungroup() %>%
arrange(region, factor(season, levels = c("Spring", "Summer", "Autumn", "Winter")))
full_seasonal_table %>%
mutate(av_items_per_1000 = round(av_items_per_1000, 2),
av_daylight = round(av_daylight, 2)) %>%
gt(groupname_col = "region") %>%
cols_label(
season = md("Season"),
av_daylight = md("Mean Total Daylight (hrs)"),
av_items_per_1000 = md("Mean Prescriptions (units/1000 people)")) %>%
tab_header(
title = md("Antidepressant Prescriptions per 1000 and Total Daylight hours by region"),
subtitle = "March 1st, 2024 - February 28th, 2025") %>%
fmt_number(columns = c(av_items_per_1000, av_daylight), decimals = 2)
| Antidepressant Prescriptions per 1000 and Total Daylight hours by region | ||
| March 1st, 2024 - February 28th, 2025 | ||
| Season | Mean Total Daylight (hrs) | Mean Prescriptions (units/1000 people) |
|---|---|---|
| East | ||
| Spring | 361.80 | 113.06 |
| Summer | 452.60 | 113.84 |
| Autumn | 268.60 | 113.79 |
| Winter | 163.60 | 114.51 |
| North | ||
| Spring | 374.60 | 121.67 |
| Summer | 330.40 | 120.00 |
| Autumn | 229.70 | 121.45 |
| Winter | 121.90 | 121.96 |
| West | ||
| Spring | 367.40 | 137.25 |
| Summer | 403.10 | 138.44 |
| Autumn | 230.30 | 138.47 |
| Winter | 137.20 | 139.04 |
This table shows the mean seasonal total daylight hours and the mean antidepressant prescriptions per 1000 population for each region. This is the numeric anchor for the following data visualisations: a) regional dual bar seasonal plot b) seasonal heat map c) deprivation vs prescribing scatter plot
Figure 1 shows a dual bar chart graph displaying antidepressant prescriptions per 1000 people proportional to each NHS Health Board population and average daylight hours per month from March 2024 to March 2025, faceted by Scottish Geographical Region (North, East, and West).
dual_bar_analysis <- analysis_prescr_smid %>%
group_by(region, season) %>%
summarise(av_items_per_1000 = mean(items_per_1000, na.rm = TRUE), av_daylight = mean(daylight_hrs, na.rm = TRUE)) %>%
ungroup() %>%
mutate(season = factor(season, levels = c("Spring", "Summer", "Autumn", "Winter"))) %>%
pivot_longer(cols = c(av_daylight, av_items_per_1000),
names_to = "variable",
values_to = "value")
# Making a ggplot with double bars
dual_bar_plot <- dual_bar_analysis %>%
ggplot(aes(x = season, y = value, fill = variable, text = ifelse(
variable == "av_daylight",
paste0("Daylight (hrs): ", round(value, 2)),
paste0("Prescriptions: ", round(value, 2))))) +
geom_col(position = position_dodge(width = 0.9), alpha = 0.8) +
facet_wrap(~region, nrow = 1, scales = "free_x") +
scale_y_continuous(
name = str_wrap("Average Total Daylight (hrs)", width = 30),
breaks = seq(0, max(dual_bar_analysis$value), 50),
sec.axis = sec_axis(~ ., name = str_wrap("Average Antidepressant Prescriptions (units/1000 people)", width = 35), breaks = seq(0, max(dual_bar_analysis$value), 50))) +
scale_fill_manual(values = c("orange", "skyblue"),
labels = c("Daylight (hrs)", "Prescriptions (units/1000)")) +
labs(
title = str_wrap("Average seasonal total daylight hours and average antidepressant prescription items by region", width = 63),
subtitle = "Prescriptions per 1000 people across Scottish Health Boards",
x = "Season",
fill = "") +
theme_minimal(base_size = 13) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "top",
legend.title = element_blank(),
legend.text = element_text(size = 10),
strip.background = element_rect(fill = "gray90", color = NA),
strip.text = element_text(face = "bold", size = 12),
plot.title = element_text(face = "bold", size = 16, margin = margin(b = 5), hjust = 0.3),
plot.title.position = "panel",
plot.subtitle = element_text(face = "bold", size = 12, margin = margin(b = 10), hjust = 0.5),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
plot.margin = margin(t = 20, r = 10, b = 40, l = 10, unit = "pt"))
dual_bar_plot
Prescriptions seem to marginally increase from Spring to Winter. However, summer seasons across regions don’t show the lowest average antidepressant prescriptions. It seems that antidepressant prescription trends increase as the seasonal year progresses.
Figure 2 shows geospatial visualisation of prescriptions per season per Health Board, where two choropleth maps have been faceted to facilitate seasonal comparisons.
map_prescr_seasons <- analysis_geo_prescr %>%
ggplot() +
geom_sf(aes(fill = av_items_per_1000), size = 0.15, color = "darkgrey") +
scale_fill_distiller(palette = "Blues", direction = 1) +
facet_wrap(~season, nrow = 1) +
labs(title = "Seasonal Antidepressant Prescriptions (March 2024 - February 2025)", subtitle = "Prescriptions by Scottish Health Board per 1000 people comparable with Average Total Daylight (hrs)", fill = "units/1000 people") +
theme_void() +
theme(
plot.title = element_text(face = "bold", size = 10, hjust = 0.5),
plot.subtitle = element_text(size = 9, margin = margin(t = 10, b = 20), hjust = 0.5),
legend.title = element_text(face = "bold", size = 10))
map_daylight_seasons <- analysis_geo_prescr %>%
ggplot() +
geom_sf(aes(fill = av_daylight), size = 0.15, colour = "darkgrey") +
scale_fill_distiller(palette = "Oranges", direction = 1) +
facet_wrap(~season, nrow = 1) +
labs(fill = "Av. Total Daylight (hrs)") +
theme_void() +
theme(legend.title = element_text(face = "bold", size = 10))
full_map_plot <- map_prescr_seasons / map_daylight_seasons +
plot_layout(heights = c(1,1))
full_map_plot
Seems like the southernmost Health Boards are situated, the higher
the anitdepressant prescriptions per 1000 people there are. The
choropleth map seems to show this as almost uniform despite seasonal
changes despite varying daylight hours. The northernmost regions are not
the ones with most prescriptions despite having the lowest total average
daylight hours overall.
With this in mind, this investigation finishes with the alternative
investigation of the third alternative hypothesis postulated initially
about SMID ranks and antidepressant prescriptions during varying
daylight seasons
There is evidence suggesting that antidepressant prescriptions
increase during the winter period in several Health Board regions across
Scotland. However, this remains an inference as statistical testing must
be done to confirm significant differences between seasons.
Furthermore, Health board regional (latitudinal) differences exist.
Northernmost boards with the least total average daylight show seasonal
patterns most strikingly when comparing summer and winter data. These
are visually consistent with the hypothesis.
Finally, deprived areas (lower SIMD rank) somewhat shows higher baseline
prescribing. However, the introduction of this factor demonstrate that
Deprivation superimposes seasonality, suggesting the social factors are
also equally if not more important than environmental factors when
researching depressive disorders within the population as a whole.
This is a promising scope of study, hence, future research must be done to tackle how Seasonal Affective Disorder (SAD) tackles Scottish residence heterogeneously. Next steps are the following: - The study must be replicated over multiple years (5-10 years) to assess if these trends stand, or more so, if these have changed over time. This is elemental for the introduction of other socio-environmental factors to the study. - GP practice as datazones could be used to reduce bias and generalisations when summarising data. - As suggested beforehand, there are multiple factors affecting SAD (i.e. age demographics, prescription policies, etc), hence future studies should: - Control for compounding factors - Consider distinct research models. i.e. Mixed effects models
Spring: March, April, May
Summer: June, July, August
Autumn: September, October, November
Winter: December, January, February
This categorisation was made according to the Met Office’s territorial delineation of Scotland based on the distribution of climate measurements as available in their website.
Northern Scotland: NHS Highland, NHS Western Isles,
NHS Orkney, NHS Shetland
Eastern Scotland: NHS Borders, NHS Lothian, NHS Fife,
NHS Tayside, NHS Grampian, NHS Forth Valley
Western Scotland: NHS Ayrshire and Arran, NHS Dumfries
and Galloway, NHS Greater Glasgow and Clyde, NHS Lanarkshire
Northern Scotland:
https://www.metoffice.gov.uk/pub/data/weather/uk/climate/datasets/Sunshine/ranked/Scotland_N.txt
Eastern Scotland:
https://www.metoffice.gov.uk/pub/data/weather/uk/climate/datasets/Sunshine/ranked/Scotland_E.txt
Western Scotland:
https://www.metoffice.gov.uk/pub/data/weather/uk/climate/datasets/Sunshine/ranked/Scotland_W.txt
# Interactive scatter plot
scatter_plot_data <- analysis_geo_prescr %>%
st_drop_geometry() %>%
filter(!is.na(SMID_median_rank), !is.na(av_items_per_1000))
scatter_plot <- plot_ly(
data = scatter_plot_data,
x = ~SMID_median_rank,
y = ~av_items_per_1000,
type = "scatter",
mode = "markers",
color = ~hb_name,
text = ~paste0(
"HB:NHS ", hb_name, "<br>",
"Season: ", season, "<br>",
"Prescriptions/1000: ", round(av_items_per_1000,1), "<br>",
"Av. Total Daylight (hrs): ", round(av_daylight, 1)),
hoverinfo = "text",
marker = list(size = 10, opacity = 0.8)) %>%
layout(
title = list(text = "Antidepressant Prescriptions according to Median Deprivation Ranks",
font = list(size = 14),
x = 0.5),
annotations = list(list(
x = 0.5,
y = 1.05,
text = "Prescriptions per 1000 people across Scottish NHS Health Boards | Scottish Multiple Index of Deprivation (SMID) Rank",
xref = "paper",
yref = "paper",
showarrow = FALSE,
font = list(size = 10),
xanchor = "center",
yanchor = "bottom")),
xaxis = list(title = "SMID Rank (Higher = Less Deprived)"),
yaxis = list(title = "Antidepressant Prescriptions (units/1000)"),
margin = list (t = 90, r = 100, b = 85, l = 85))
scatter_plot